Misogynistic Extremism: A Scoping Review.

In recent years, the concept of "misogynistic extremism" has emerged as a subject of interest among scholars, governments, law enforcement personnel, and the media. Yet a consistent understanding of how misogynistic extremism is defined and conceptualized has not yet emerged. Varying epistemological orientations may contribute to the current conceptual muddle of this topic, reflecting long-standing and on-going challenges with the conceptualization of its individual components. To address the potential impact of misogynistic extremism (i.e., violent attacks), a more precise understanding of what this phenomenon entails is needed. To summarize the existing knowledge base on the nature of misogynistic extremism, this scoping review analyzed publications within English-language peer-reviewed and gray literature sources. Seven electronic databases and citation indexes were systematically searched using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) checklist and charted using the 2020 PRISMA flow diagram. Inclusion criteria included English peer-reviewed articles and relevant gray literature publications, which contained the term "misogynistic extremism" and other closely related terms. No date restrictions were imposed. The search strategy initially yielded 475 publications. After exclusion of ineligible articles, 40 publications remained for synthesis. We found that misogynistic extremism is most frequently conceptualized in the context of misogynistic incels, male supremacism, far-right extremism, terrorism, and the black pill ideology. Policy recommendations include increased education among law enforcement and Countering and Preventing Violent Extremism experts on male supremacist violence and encouraging legal and educational mechanisms to bolster gender equality. Violence stemming from misogynistic worldviews must be addressed by directly acknowledging and challenging socially embedded systems of oppression such as white supremacy and cisheteropatriarchy.

Nowhere to Hide: Online Rumor Detection Based on Retweeting Graph Neural Networks.

Online rumor detection is crucial for a healthier online environment. Traditional methods mainly rely on content understanding. However, these contents can be easily adjusted to avoid such supervision and are insufficient to improve the detection result. Compared with the content, information propagation patterns are more informative to support further performance promotion. Unfortunately, learning the propagation patterns is difficult, since the retweeting tree is more topologically complicated than linear sequences or binary trees. In light of this, we propose a novel rumor detection framework based on structure-aware retweeting graph neural networks. To capture the propagation patterns, we first design a novel conversion method to transform the complex retweeting tree as more tractable binary tree without losing the reconstruction information. Then, we serialize the retweeting tree as a corpus of meta-tree paths, where each meta-tree can preserve a basic substructure. A deep neural network is then designed to integrate all meta-trees and to generate the global structural embeddings. Furthermore, we propose to integrate content, users, and propagation patterns to enhance more reliable performance. To this end, we propose a novel self-attention-based retweeting neural network to learn individual features from both content and users. We then fuse the node-level features with our global structural embeddings via a mutual attention unit. In this way, we can generate more comprehensive representations for rumor detection. Extensive evaluations on two real-world datasets show remarkable superiorities of our model compared with existing methods.